Abstract

Given that the neural and connective tissues of the optic nerve head (ONH) exhibit complex morphological changes with the development and progression of glaucoma, their simultaneous isolation from optical coherence tomography (OCT) images may be of great interest for the clinical diagnosis and management of this pathology. A deep learning algorithm (custom U-NET) was designed and trained to segment 6 ONH tissue layers by capturing both the local (tissue texture) and contextual information (spatial arrangement of tissues). The overall Dice coefficient (mean of all tissues) was 0.91 ± 0.05 when assessed against manual segmentations performed by an expert observer. Further, we automatically extracted six clinically relevant neural and connective tissue structural parameters from the segmented tissues. We offer here a robust segmentation framework that could also be extended to the 3D segmentation of the ONH tissues.

© 2018 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

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2018 (3)

S. K. Devalla, K. S. Chin, J. M. Mari, T. A. Tun, N. G. Strouthidis, T. Aung, A. H. Thiéry, and M. J. A. Girard, “A deep learning approach to digitally stain optical coherence tomography images of the optic nerve head,” Invest. Ophthalmol. Vis. Sci. 59(1), 63–74 (2018).
[Crossref] [PubMed]

E. Hosseini-Asl, M. Ghazal, A. Mahmoud, A. Aslantas, A. M. Shalaby, M. F. Casanova, G. N. Barnes, G. Gimel’farb, R. Keynton, and A. El-Baz, “Alzheimer’s disease diagnostics by a 3D deeply supervised adaptable convolutional network,” Front. Biosci. (Landmark Ed.) 23(2), 584–596 (2018).
[Crossref] [PubMed]

T. A. Tun, E. Atalay, M. Baskaran, M. E. Nongpiur, H. M. Htoon, D. Goh, C. Y. Cheng, S. A. Perera, T. Aung, N. G. Strouthidis, and M. J. A. Girard, “Association of functional loss with the biomechanical response of the optic nerve head to acute transient intraocular pressure elevations,” JAMA Ophthalmol. 136(2), 184–192 (2018).
[Crossref] [PubMed]

2017 (7)

S. L. Mansberger, S. A. Menda, B. A. Fortune, S. K. Gardiner, and S. Demirel, “Automated segmentation errors when using optical coherence tomography to measure retinal nerve fiber layer thickness in glaucoma,” Am. J. Ophthalmol. 174, 1–8 (2017).
[Crossref] [PubMed]

M. S. Miri, M. D. Abràmoff, Y. H. Kwon, M. Sonka, and M. K. Garvin, “A machine-learning graph-based approach for 3D segmentation of Bruch’s membrane opening from glaucomatous SD-OCT volumes,” Med. Image Anal. 39, 206–217 (2017).
[Crossref] [PubMed]

C. T. S. Rueden, J. Schindelin, M. C. Hiner, B. E. DeZonia, A. E. Walter, E. T. Arena, and K. W. Eliceiri, “ImageJ2: ImageJ for the next generation of scientific image data,” BMC Bioinformatics 18(1), 529 (2017).
[Crossref] [PubMed]

J.-M. Mari, T. Aung, C.-Y. Cheng, N. G. Strouthidis, and M. J. A. Girard, “A digital staining algorithm for optical coherence tomography images of the optic nerve head,” Transl. Vis. Sci. Technol. 6(1), 8 (2017).
[Crossref] [PubMed]

L. Fang, D. Cunefare, C. Wang, R. H. Guymer, S. Li, and S. Farsiu, “Automatic segmentation of nine retinal layer boundaries in OCT images of non-exudative AMD patients using deep learning and graph search,” Biomed. Opt. Express 8(5), 2732–2744 (2017).
[Crossref] [PubMed]

F. G. Venhuizen, B. van Ginneken, B. Liefers, M. J. J. P. van Grinsven, S. Fauser, C. Hoyng, T. Theelen, and C. I. Sánchez, “Robust total retina thickness segmentation in optical coherence tomography images using convolutional neural networks,” Biomed. Opt. Express 8(7), 3292–3316 (2017).
[Crossref] [PubMed]

X. Sui, Y. Zheng, B. Wei, H. Bi, J. Wu, X. Pan, Y. Yin, and S. Zhang, “Choroid segmentation from Optical Coherence Tomography with graph-edge weights learned from deep convolutional neural networks,” Neurocomputing 237, 332–341 (2017).
[Crossref]

2016 (12)

R. A. Alshareef, S. Dumpala, S. Rapole, M. Januwada, A. Goud, H. K. Peguda, and J. Chhablani, “Prevalence and distribution of segmentation errors in macular ganglion cell analysis of healthy eyes using cirrus HD-OCT,” PLoS One 11(5), e0155319 (2016).
[Crossref] [PubMed]

C. Ye, M. Yu, and C. K. Leung, “Impact of segmentation errors and retinal blood vessels on retinal nerve fibre layer measurements using spectral-domain optical coherence tomography,” Acta Ophthalmol. 94(3), e211–e219 (2016).
[Crossref] [PubMed]

A. Lang, A. Carass, B. M. Jedynak, S. D. Solomon, P. A. Calabresi, and J. L. Prince, “Intensity inhomogeneity correction of macular OCT using N3 and retinal flatspace,” Proc. IEEE Int. Symp. Biomed. Imaging 2016, 197–200 (2016).
[PubMed]

J. M. D. Gmeiner, W. A. Schrems, C. Y. Mardin, R. Laemmer, F. E. Kruse, and L. M. Schrems-Hoesl, “Comparison of Bruch’s membrane opening minimum rim width and peripapillary retinal nerve fiber layer thickness in early glaucoma assessment BMO-MRW and RNFLT in early glaucoma assessment,” Invest. Ophthalmol. Vis. Sci. 57, 575–584 (2016).
[Crossref]

Z. Lin, S. Huang, B. Xie, and Y. Zhong, “Peripapillary choroidal thickness and open-angle glaucoma: a meta-analysis,” J. Ophthalmol. 2016, 1 (2016).
[Crossref] [PubMed]

Y. W. Kim, J. W. Jeoung, D. W. Kim, M. J. A. Girard, J. M. Mari, K. H. Park, and D. M. Kim, “Clinical assessment of lamina cribrosa curvature in eyes with primary open-angle glaucoma,” PLoS One 11(3), e0150260 (2016).
[Crossref] [PubMed]

S. K. Gardiner, S. Demirel, J. Reynaud, and B. Fortune, “Changes in retinal nerve fiber layer reflectance intensity as a predictor of functional progression in glaucoma,” Invest. Ophthalmol. Vis. Sci. 57(3), 1221–1227 (2016).
[Crossref] [PubMed]

N. Tajbakhsh, J. Y. Shin, S. R. Gurudu, R. T. Hurst, C. B. Kendall, M. B. Gotway, and Jianming Liang, “Convolutional neural networks for medical image analysis: full training or fine tuning?” IEEE Trans. Med. Imaging 35(5), 1299–1312 (2016).
[Crossref] [PubMed]

J. C. Han, D. Y. Choi, Y. K. Kwun, W. Suh, and C. Kee, “Evaluation of lamina cribrosa thickness and depth in ocular hypertension,” Jpn. J. Ophthalmol. 60(1), 14–19 (2016).
[Crossref] [PubMed]

M. J. Girard, M. R. Beotra, K. S. Chin, A. Sandhu, M. Clemo, E. Nikita, D. S. Kamal, M. Papadopoulos, J. M. Mari, T. Aung, and N. G. Strouthidis, “In vivo 3-dimensional strain mapping of the optic nerve head following intraocular pressure lowering by trabeculectomy,” Ophthalmology 123(6), 1190–1200 (2016).
[Crossref] [PubMed]

T. A. Tun, S. G. Thakku, O. Png, M. Baskaran, H. M. Htoon, S. Sharma, M. E. Nongpiur, C. Y. Cheng, T. Aung, N. G. Strouthidis, and M. J. Girard, “Shape changes of the anterior lamina cribrosa in normal, ocular hypertensive, and glaucomatous eyes following acute intraocular pressure elevation,” Invest. Ophthalmol. Vis. Sci. 57(11), 4869–4877 (2016).
[Crossref] [PubMed]

X. Wang, M. R. Beotra, T. A. Tun, M. Baskaran, S. Perera, T. Aung, N. G. Strouthidis, D. Milea, and M. J. A. Girard, “In vivo 3-dimensional strain mapping confirms large optic nerve head deformations following horizontal eye movements,” Invest. Ophthalmol. Vis. Sci. 57(13), 5825–5833 (2016).
[Crossref] [PubMed]

2015 (11)

R. Y. Abe, C. P. B. Gracitelli, A. Diniz-Filho, A. J. Tatham, and F. A. Medeiros, “Lamina Cribrosa in Glaucoma: Diagnosis and Monitoring,” Curr. Ophthalmol. Rep. 3(2), 74–84 (2015).
[Crossref] [PubMed]

M. J. A. Girard, T. A. Tun, R. Husain, S. Acharyya, B. A. Haaland, X. Wei, J. M. Mari, S. A. Perera, M. Baskaran, T. Aung, and N. G. Strouthidis, “Lamina cribrosa visibility using optical coherence tomography: comparison of devices and effects of image enhancement techniques,” Invest. Ophthalmol. Vis. Sci. 56(2), 865–874 (2015).
[Crossref] [PubMed]

I. C. Campbell, B. Coudrillier, J. Mensah, R. L. Abel, and C. R. Ethier, “Automated segmentation of the lamina cribrosa using Frangi’s filter: a novel approach for rapid identification of tissue volume fraction and beam orientation in a trabeculated structure in the eye,” J. R. Soc. Interface 12(104), 20141009 (2015).
[Crossref] [PubMed]

M. H. Tan, S. H. Ong, S. G. Thakku, C.-Y. Cheng, T. Aung, and M. Girard, “Automatic feature extraction of optical coherence tomography for lamina cribrosa detection,” Journal of Image and Graphics 3, 2 (2015)

S. G. Thakku, Y. C. Tham, M. Baskaran, J. M. Mari, N. G. Strouthidis, T. Aung, C. Y. Cheng, and M. J. Girard, “A global shape index to characterize anterior lamina cribrosa morphology and its determinants in healthy indian eyes,” Invest. Ophthalmol. Vis. Sci. 56(6), 3604–3614 (2015).
[Crossref] [PubMed]

Y. Sawada, M. Hangai, K. Murata, M. Ishikawa, and T. Yoshitomi, “Lamina cribrosa depth variation measured by spectral-domain optical coherence tomography within and between four glaucomatous optic disc phenotypes,” Invest. Ophthalmol. Vis. Sci. 56(10), 5777–5784 (2015).
[Crossref] [PubMed]

S. C. Park, J. Brumm, R. L. Furlanetto, C. Netto, Y. Liu, C. Tello, J. M. Liebmann, and R. Ritch, “Lamina Cribrosa Depth in Different Stages of Glaucoma,” Invest. Ophthalmol. Vis. Sci. 56(3), 2059–2064 (2015).
[Crossref] [PubMed]

Z. Wu, G. Xu, R. N. Weinreb, M. Yu, and C. K. Leung, “Optic nerve head deformation in glaucoma: a prospective analysis of optic nerve head surface and lamina cribrosa surface displacement,” Ophthalmology 122(7), 1317–1329 (2015).
[Crossref] [PubMed]

M. J. Girard, T. A. Tun, R. Husain, S. Acharyya, B. A. Haaland, X. Wei, J. M. Mari, S. A. Perera, M. Baskaran, T. Aung, and N. G. Strouthidis, “Lamina cribrosa visibility using optical coherence tomography: comparison of devices and effects of image enhancement techniques,” Invest. Ophthalmol. Vis. Sci. 56(2), 865–874 (2015).
[Crossref] [PubMed]

K. E. Kim, J. W. Jeoung, K. H. Park, D. M. Kim, and S. H. Kim, “Diagnostic classification of macular ganglion cell and retinal nerve fiber layer analysis: differentiation of false-positives from glaucoma,” Ophthalmology 122(3), 502–510 (2015).
[Crossref] [PubMed]

Y. Liu, H. Simavli, C. J. Que, J. L. Rizzo, E. Tsikata, R. Maurer, and T. C. Chen, “Patient characteristics associated with artifacts in Spectralis optical coherence tomography imaging of the retinal nerve fiber layer in glaucoma,” Am. J. Ophthalmol. 159(3), 565–576 (2015).
[Crossref] [PubMed]

2014 (8)

S. Asrani, L. Essaid, B. D. Alder, and C. Santiago-Turla, “Artifacts in spectral-domain optical coherence tomography measurements in glaucoma,” JAMA Ophthalmol. 132(4), 396–402 (2014).
[Crossref] [PubMed]

J. B. Jonas, “Glaucoma and choroidal thickness,” J. Ophthalmic Vis. Res. 9(2), 151–153 (2014).
[PubMed]

A. Miki, F. A. Medeiros, R. N. Weinreb, S. Jain, F. He, L. Sharpsten, N. Khachatryan, N. Hammel, J. M. Liebmann, C. A. Girkin, P. A. Sample, and L. M. Zangwill, “Rates of retinal nerve fiber layer thinning in glaucoma suspect eyes,” Ophthalmology 121(7), 1350–1358 (2014).
[Crossref] [PubMed]

K. M. Lee, T. W. Kim, R. N. Weinreb, E. J. Lee, M. J. A. Girard, and J. M. Mari, “Anterior lamina cribrosa insertion in primary open-angle glaucoma patients and healthy subjects,” PLoS One 9(12), e114935 (2014).
[Crossref] [PubMed]

F. A. Almobarak, N. O’Leary, A. S. C. Reis, G. P. Sharpe, D. M. Hutchison, M. T. Nicolela, and B. C. Chauhan, “Automated segmentation of optic nerve head structures with optical coherence tomography,” Invest. Ophthalmol. Vis. Sci. 55(2), 1161–1168 (2014).
[Crossref] [PubMed]

S. Niu, Q. Chen, L. de Sisternes, D. L. Rubin, W. Zhang, and Q. Liu, “Automated retinal layers segmentation in SD-OCT images using dual-gradient and spatial correlation smoothness constraint,” Comput. Biol. Med. 54, 116–128 (2014).
[Crossref] [PubMed]

E. J. Lee, T. W. Kim, M. Kim, M. J. A. Girard, J. M. Mari, and R. N. Weinreb, “Recent structural alteration of the peripheral lamina cribrosa near the location of disc hemorrhage in glaucoma,” Invest. Ophthalmol. Vis. Sci. 55(4), 2805–2815 (2014).
[Crossref] [PubMed]

P. R. Bhagat, K. V. Deshpande, and B. Natu, “Utility of ganglion cell complex analysis in early diagnosis and monitoring of glaucoma using a different spectral domain optical coherence tomography,” J Curr Glaucoma Pract 8(3), 101–106 (2014).
[Crossref] [PubMed]

2013 (6)

J. Y. You, S. C. Park, D. Su, C. C. Teng, J. M. Liebmann, and R. Ritch, “Focal lamina cribrosa defects associated with glaucomatous rim thinning and acquired pits,” JAMA Ophthalmol. 131(3), 314–320 (2013).
[Crossref] [PubMed]

D. Alonso-Caneiro, S. A. Read, and M. J. Collins, “Automatic segmentation of choroidal thickness in optical coherence tomography,” Biomed. Opt. Express 4(12), 2795–2812 (2013).
[Crossref] [PubMed]

J. Tian, P. Marziliano, M. Baskaran, T. A. Tun, and T. Aung, “Automatic segmentation of the choroid in enhanced depth imaging optical coherence tomography images,” Biomed. Opt. Express 4(3), 397–411 (2013).
[Crossref] [PubMed]

A. Lang, A. Carass, M. Hauser, E. S. Sotirchos, P. A. Calabresi, H. S. Ying, and J. L. Prince, “Retinal layer segmentation of macular OCT images using boundary classification,” Biomed. Opt. Express 4(7), 1133–1152 (2013).
[Crossref] [PubMed]

M. J. Girard, N. G. Strouthidis, A. Desjardins, J. M. Mari, and C. R. Ethier, “In vivo optic nerve head biomechanics: performance testing of a three-dimensional tracking algorithm,” J. R. Soc. Interface 10(87), 20130459 (2013).
[Crossref] [PubMed]

J. M. Mari, N. G. Strouthidis, S. C. Park, and M. J. A. Girard, “Enhancement of lamina cribrosa visibility in optical coherence tomography images using adaptive compensation,” Invest. Ophthalmol. Vis. Sci. 54(3), 2238–2247 (2013).
[Crossref] [PubMed]

2012 (1)

L. Zhang, K. Lee, M. Niemeijer, R. F. Mullins, M. Sonka, and M. D. Abràmoff, “Automated segmentation of the choroid from clinical SD-OCT,” Invest. Ophthalmol. Vis. Sci. 53(12), 7510–7519 (2012).
[Crossref] [PubMed]

2011 (3)

H. Yang, G. Williams, J. C. Downs, I. A. Sigal, M. D. Roberts, H. Thompson, and C. F. Burgoyne, “Posterior (outward) migration of the lamina cribrosa and early cupping in monkey experimental glaucoma,” Invest. Ophthalmol. Vis. Sci. 52(10), 7109–7121 (2011).
[Crossref] [PubMed]

I. A. Sigal, H. Yang, M. D. Roberts, J. L. Grimm, C. F. Burgoyne, S. Demirel, and J. C. Downs, “IOP-induced lamina cribrosa deformation and scleral canal expansion: independent or related?” Invest. Ophthalmol. Vis. Sci. 52(12), 9023–9032 (2011).
[Crossref] [PubMed]

N. Fan, N. Huang, D. S. C. Lam, and C. K.-S. Leung, “Measurement of photoreceptor layer in glaucoma: a spectral-domain optical coherence tomography study,” J. Ophthalmol. 2011, 264803 (2011).
[Crossref] [PubMed]

2010 (2)

N. G. Strouthidis, J. Grimm, G. A. Williams, G. A. Cull, D. J. Wilson, and C. F. Burgoyne, “A comparison of optic nerve head morphology viewed by spectral domain optical coherence tomography and by serial histology,” Invest. Ophthalmol. Vis. Sci. 51(3), 1464–1474 (2010).
[Crossref] [PubMed]

M. A. Mayer, J. Hornegger, C. Y. Mardin, and R. P. Tornow, “Retinal Nerve Fiber Layer Segmentation on FD-OCT Scans of Normal Subjects and Glaucoma Patients,” Biomed. Opt. Express 1(5), 1358–1383 (2010).
[Crossref] [PubMed]

2009 (1)

B. Al-Diri, A. Hunter, and D. Steel, “An active contour model for segmenting and measuring retinal vessels,” IEEE Trans. Med. Imaging 28(9), 1488–1497 (2009).
[Crossref] [PubMed]

2007 (1)

T. Ojima, T. Tanabe, M. Hangai, S. Yu, S. Morishita, and N. Yoshimura, “Measurement of retinal nerve fiber layer thickness and macular volume for glaucoma detection using optical coherence tomography,” Jpn. J. Ophthalmol. 51(3), 197–203 (2007).
[Crossref] [PubMed]

2001 (1)

J. C. Downs, M. E. Ensor, A. J. Bellezza, H. W. Thompson, R. T. Hart, and C. F. Burgoyne, “Posterior scleral thickness in perfusion-fixed normal and early-glaucoma monkey eyes,” Invest. Ophthalmol. Vis. Sci. 42(13), 3202–3208 (2001).
[PubMed]

2000 (1)

C. Bowd, R. N. Weinreb, J. M. Williams, and L. M. Zangwill, “The retinal nerve fiber layer thickness in ocular hypertensive, normal, and glaucomatous eyes with optical coherence tomography,” Arch. Ophthalmol. 118(1), 22–26 (2000).
[Crossref] [PubMed]

1981 (2)

H. A. Quigley and E. M. Addicks, “Regional differences in the structure of the lamina cribrosa and their relation to glaucomatous optic nerve damage,” Arch. Ophthalmol. 99(1), 137–143 (1981).
[Crossref] [PubMed]

H. A. Quigley, E. M. Addicks, W. R. Green, and A. E. Maumenee, “Optic nerve damage in human glaucoma. II. The site of injury and susceptibility to damage,” Arch. Ophthalmol. 99(4), 635–649 (1981).
[Crossref] [PubMed]

Abe, R. Y.

R. Y. Abe, C. P. B. Gracitelli, A. Diniz-Filho, A. J. Tatham, and F. A. Medeiros, “Lamina Cribrosa in Glaucoma: Diagnosis and Monitoring,” Curr. Ophthalmol. Rep. 3(2), 74–84 (2015).
[Crossref] [PubMed]

Abel, R. L.

I. C. Campbell, B. Coudrillier, J. Mensah, R. L. Abel, and C. R. Ethier, “Automated segmentation of the lamina cribrosa using Frangi’s filter: a novel approach for rapid identification of tissue volume fraction and beam orientation in a trabeculated structure in the eye,” J. R. Soc. Interface 12(104), 20141009 (2015).
[Crossref] [PubMed]

Abràmoff, M. D.

M. S. Miri, M. D. Abràmoff, Y. H. Kwon, M. Sonka, and M. K. Garvin, “A machine-learning graph-based approach for 3D segmentation of Bruch’s membrane opening from glaucomatous SD-OCT volumes,” Med. Image Anal. 39, 206–217 (2017).
[Crossref] [PubMed]

L. Zhang, K. Lee, M. Niemeijer, R. F. Mullins, M. Sonka, and M. D. Abràmoff, “Automated segmentation of the choroid from clinical SD-OCT,” Invest. Ophthalmol. Vis. Sci. 53(12), 7510–7519 (2012).
[Crossref] [PubMed]

Acharyya, S.

M. J. A. Girard, T. A. Tun, R. Husain, S. Acharyya, B. A. Haaland, X. Wei, J. M. Mari, S. A. Perera, M. Baskaran, T. Aung, and N. G. Strouthidis, “Lamina cribrosa visibility using optical coherence tomography: comparison of devices and effects of image enhancement techniques,” Invest. Ophthalmol. Vis. Sci. 56(2), 865–874 (2015).
[Crossref] [PubMed]

M. J. Girard, T. A. Tun, R. Husain, S. Acharyya, B. A. Haaland, X. Wei, J. M. Mari, S. A. Perera, M. Baskaran, T. Aung, and N. G. Strouthidis, “Lamina cribrosa visibility using optical coherence tomography: comparison of devices and effects of image enhancement techniques,” Invest. Ophthalmol. Vis. Sci. 56(2), 865–874 (2015).
[Crossref] [PubMed]

Addicks, E. M.

H. A. Quigley and E. M. Addicks, “Regional differences in the structure of the lamina cribrosa and their relation to glaucomatous optic nerve damage,” Arch. Ophthalmol. 99(1), 137–143 (1981).
[Crossref] [PubMed]

H. A. Quigley, E. M. Addicks, W. R. Green, and A. E. Maumenee, “Optic nerve damage in human glaucoma. II. The site of injury and susceptibility to damage,” Arch. Ophthalmol. 99(4), 635–649 (1981).
[Crossref] [PubMed]

Ahmed, A.

S. Naz, A. Ahmed, M. U. Akram, and S. A. Khan, “Automated segmentation of RPE layer for the detection of age macular degeneration using OCT images,” in Proceedings of Sixth International Conference on Image Processing Theory, Tools and Applications (IPTA), 2016), 1–4.
[Crossref]

Akram, M. U.

S. Naz, A. Ahmed, M. U. Akram, and S. A. Khan, “Automated segmentation of RPE layer for the detection of age macular degeneration using OCT images,” in Proceedings of Sixth International Conference on Image Processing Theory, Tools and Applications (IPTA), 2016), 1–4.
[Crossref]

Al-Bander, B.

B. Al-Bander, B. M. Williams, M. A. Al-Taee, W. Al-Nuaimy, and Y. Zheng, “A novel choroid segmentation method for retinal diagnosis using deep learning,” in Proceedings of 10th International Conference on Developments in eSystems Engineering (DeSE) (2017), 182–187.
[Crossref]

Alder, B. D.

S. Asrani, L. Essaid, B. D. Alder, and C. Santiago-Turla, “Artifacts in spectral-domain optical coherence tomography measurements in glaucoma,” JAMA Ophthalmol. 132(4), 396–402 (2014).
[Crossref] [PubMed]

Al-Diri, B.

B. Al-Diri, A. Hunter, and D. Steel, “An active contour model for segmenting and measuring retinal vessels,” IEEE Trans. Med. Imaging 28(9), 1488–1497 (2009).
[Crossref] [PubMed]

Almobarak, F. A.

F. A. Almobarak, N. O’Leary, A. S. C. Reis, G. P. Sharpe, D. M. Hutchison, M. T. Nicolela, and B. C. Chauhan, “Automated segmentation of optic nerve head structures with optical coherence tomography,” Invest. Ophthalmol. Vis. Sci. 55(2), 1161–1168 (2014).
[Crossref] [PubMed]

Al-Nuaimy, W.

B. Al-Bander, B. M. Williams, M. A. Al-Taee, W. Al-Nuaimy, and Y. Zheng, “A novel choroid segmentation method for retinal diagnosis using deep learning,” in Proceedings of 10th International Conference on Developments in eSystems Engineering (DeSE) (2017), 182–187.
[Crossref]

Alonso-Caneiro, D.

Alshareef, R. A.

R. A. Alshareef, S. Dumpala, S. Rapole, M. Januwada, A. Goud, H. K. Peguda, and J. Chhablani, “Prevalence and distribution of segmentation errors in macular ganglion cell analysis of healthy eyes using cirrus HD-OCT,” PLoS One 11(5), e0155319 (2016).
[Crossref] [PubMed]

Al-Taee, M. A.

B. Al-Bander, B. M. Williams, M. A. Al-Taee, W. Al-Nuaimy, and Y. Zheng, “A novel choroid segmentation method for retinal diagnosis using deep learning,” in Proceedings of 10th International Conference on Developments in eSystems Engineering (DeSE) (2017), 182–187.
[Crossref]

Arena, E. T.

C. T. S. Rueden, J. Schindelin, M. C. Hiner, B. E. DeZonia, A. E. Walter, E. T. Arena, and K. W. Eliceiri, “ImageJ2: ImageJ for the next generation of scientific image data,” BMC Bioinformatics 18(1), 529 (2017).
[Crossref] [PubMed]

Aslantas, A.

E. Hosseini-Asl, M. Ghazal, A. Mahmoud, A. Aslantas, A. M. Shalaby, M. F. Casanova, G. N. Barnes, G. Gimel’farb, R. Keynton, and A. El-Baz, “Alzheimer’s disease diagnostics by a 3D deeply supervised adaptable convolutional network,” Front. Biosci. (Landmark Ed.) 23(2), 584–596 (2018).
[Crossref] [PubMed]

Asrani, S.

S. Asrani, L. Essaid, B. D. Alder, and C. Santiago-Turla, “Artifacts in spectral-domain optical coherence tomography measurements in glaucoma,” JAMA Ophthalmol. 132(4), 396–402 (2014).
[Crossref] [PubMed]

Atalay, E.

T. A. Tun, E. Atalay, M. Baskaran, M. E. Nongpiur, H. M. Htoon, D. Goh, C. Y. Cheng, S. A. Perera, T. Aung, N. G. Strouthidis, and M. J. A. Girard, “Association of functional loss with the biomechanical response of the optic nerve head to acute transient intraocular pressure elevations,” JAMA Ophthalmol. 136(2), 184–192 (2018).
[Crossref] [PubMed]

Aung, T.

T. A. Tun, E. Atalay, M. Baskaran, M. E. Nongpiur, H. M. Htoon, D. Goh, C. Y. Cheng, S. A. Perera, T. Aung, N. G. Strouthidis, and M. J. A. Girard, “Association of functional loss with the biomechanical response of the optic nerve head to acute transient intraocular pressure elevations,” JAMA Ophthalmol. 136(2), 184–192 (2018).
[Crossref] [PubMed]

S. K. Devalla, K. S. Chin, J. M. Mari, T. A. Tun, N. G. Strouthidis, T. Aung, A. H. Thiéry, and M. J. A. Girard, “A deep learning approach to digitally stain optical coherence tomography images of the optic nerve head,” Invest. Ophthalmol. Vis. Sci. 59(1), 63–74 (2018).
[Crossref] [PubMed]

J.-M. Mari, T. Aung, C.-Y. Cheng, N. G. Strouthidis, and M. J. A. Girard, “A digital staining algorithm for optical coherence tomography images of the optic nerve head,” Transl. Vis. Sci. Technol. 6(1), 8 (2017).
[Crossref] [PubMed]

X. Wang, M. R. Beotra, T. A. Tun, M. Baskaran, S. Perera, T. Aung, N. G. Strouthidis, D. Milea, and M. J. A. Girard, “In vivo 3-dimensional strain mapping confirms large optic nerve head deformations following horizontal eye movements,” Invest. Ophthalmol. Vis. Sci. 57(13), 5825–5833 (2016).
[Crossref] [PubMed]

M. J. Girard, M. R. Beotra, K. S. Chin, A. Sandhu, M. Clemo, E. Nikita, D. S. Kamal, M. Papadopoulos, J. M. Mari, T. Aung, and N. G. Strouthidis, “In vivo 3-dimensional strain mapping of the optic nerve head following intraocular pressure lowering by trabeculectomy,” Ophthalmology 123(6), 1190–1200 (2016).
[Crossref] [PubMed]

T. A. Tun, S. G. Thakku, O. Png, M. Baskaran, H. M. Htoon, S. Sharma, M. E. Nongpiur, C. Y. Cheng, T. Aung, N. G. Strouthidis, and M. J. Girard, “Shape changes of the anterior lamina cribrosa in normal, ocular hypertensive, and glaucomatous eyes following acute intraocular pressure elevation,” Invest. Ophthalmol. Vis. Sci. 57(11), 4869–4877 (2016).
[Crossref] [PubMed]

M. J. Girard, T. A. Tun, R. Husain, S. Acharyya, B. A. Haaland, X. Wei, J. M. Mari, S. A. Perera, M. Baskaran, T. Aung, and N. G. Strouthidis, “Lamina cribrosa visibility using optical coherence tomography: comparison of devices and effects of image enhancement techniques,” Invest. Ophthalmol. Vis. Sci. 56(2), 865–874 (2015).
[Crossref] [PubMed]

S. G. Thakku, Y. C. Tham, M. Baskaran, J. M. Mari, N. G. Strouthidis, T. Aung, C. Y. Cheng, and M. J. Girard, “A global shape index to characterize anterior lamina cribrosa morphology and its determinants in healthy indian eyes,” Invest. Ophthalmol. Vis. Sci. 56(6), 3604–3614 (2015).
[Crossref] [PubMed]

M. H. Tan, S. H. Ong, S. G. Thakku, C.-Y. Cheng, T. Aung, and M. Girard, “Automatic feature extraction of optical coherence tomography for lamina cribrosa detection,” Journal of Image and Graphics 3, 2 (2015)

M. J. A. Girard, T. A. Tun, R. Husain, S. Acharyya, B. A. Haaland, X. Wei, J. M. Mari, S. A. Perera, M. Baskaran, T. Aung, and N. G. Strouthidis, “Lamina cribrosa visibility using optical coherence tomography: comparison of devices and effects of image enhancement techniques,” Invest. Ophthalmol. Vis. Sci. 56(2), 865–874 (2015).
[Crossref] [PubMed]

J. Tian, P. Marziliano, M. Baskaran, T. A. Tun, and T. Aung, “Automatic segmentation of the choroid in enhanced depth imaging optical coherence tomography images,” Biomed. Opt. Express 4(3), 397–411 (2013).
[Crossref] [PubMed]

Barnes, G. N.

E. Hosseini-Asl, M. Ghazal, A. Mahmoud, A. Aslantas, A. M. Shalaby, M. F. Casanova, G. N. Barnes, G. Gimel’farb, R. Keynton, and A. El-Baz, “Alzheimer’s disease diagnostics by a 3D deeply supervised adaptable convolutional network,” Front. Biosci. (Landmark Ed.) 23(2), 584–596 (2018).
[Crossref] [PubMed]

Baskaran, M.

T. A. Tun, E. Atalay, M. Baskaran, M. E. Nongpiur, H. M. Htoon, D. Goh, C. Y. Cheng, S. A. Perera, T. Aung, N. G. Strouthidis, and M. J. A. Girard, “Association of functional loss with the biomechanical response of the optic nerve head to acute transient intraocular pressure elevations,” JAMA Ophthalmol. 136(2), 184–192 (2018).
[Crossref] [PubMed]

X. Wang, M. R. Beotra, T. A. Tun, M. Baskaran, S. Perera, T. Aung, N. G. Strouthidis, D. Milea, and M. J. A. Girard, “In vivo 3-dimensional strain mapping confirms large optic nerve head deformations following horizontal eye movements,” Invest. Ophthalmol. Vis. Sci. 57(13), 5825–5833 (2016).
[Crossref] [PubMed]

T. A. Tun, S. G. Thakku, O. Png, M. Baskaran, H. M. Htoon, S. Sharma, M. E. Nongpiur, C. Y. Cheng, T. Aung, N. G. Strouthidis, and M. J. Girard, “Shape changes of the anterior lamina cribrosa in normal, ocular hypertensive, and glaucomatous eyes following acute intraocular pressure elevation,” Invest. Ophthalmol. Vis. Sci. 57(11), 4869–4877 (2016).
[Crossref] [PubMed]

M. J. Girard, T. A. Tun, R. Husain, S. Acharyya, B. A. Haaland, X. Wei, J. M. Mari, S. A. Perera, M. Baskaran, T. Aung, and N. G. Strouthidis, “Lamina cribrosa visibility using optical coherence tomography: comparison of devices and effects of image enhancement techniques,” Invest. Ophthalmol. Vis. Sci. 56(2), 865–874 (2015).
[Crossref] [PubMed]

S. G. Thakku, Y. C. Tham, M. Baskaran, J. M. Mari, N. G. Strouthidis, T. Aung, C. Y. Cheng, and M. J. Girard, “A global shape index to characterize anterior lamina cribrosa morphology and its determinants in healthy indian eyes,” Invest. Ophthalmol. Vis. Sci. 56(6), 3604–3614 (2015).
[Crossref] [PubMed]

M. J. A. Girard, T. A. Tun, R. Husain, S. Acharyya, B. A. Haaland, X. Wei, J. M. Mari, S. A. Perera, M. Baskaran, T. Aung, and N. G. Strouthidis, “Lamina cribrosa visibility using optical coherence tomography: comparison of devices and effects of image enhancement techniques,” Invest. Ophthalmol. Vis. Sci. 56(2), 865–874 (2015).
[Crossref] [PubMed]

J. Tian, P. Marziliano, M. Baskaran, T. A. Tun, and T. Aung, “Automatic segmentation of the choroid in enhanced depth imaging optical coherence tomography images,” Biomed. Opt. Express 4(3), 397–411 (2013).
[Crossref] [PubMed]

Belghith, A.

A. Belghith, C. Bowd, F. A. Medeiros, R. N. Weinreb, and L. M. Zangwill, “Automated segmentation of anterior lamina cribrosa surface: How the lamina cribrosa responds to intraocular pressure change in glaucoma eyes?” in Proceedings of IEEE 12th International Symposium on Biomedical Imaging (ISBI) (2015), 222–225.
[Crossref]

Bellezza, A. J.

J. C. Downs, M. E. Ensor, A. J. Bellezza, H. W. Thompson, R. T. Hart, and C. F. Burgoyne, “Posterior scleral thickness in perfusion-fixed normal and early-glaucoma monkey eyes,” Invest. Ophthalmol. Vis. Sci. 42(13), 3202–3208 (2001).
[PubMed]

Beotra, M. R.

X. Wang, M. R. Beotra, T. A. Tun, M. Baskaran, S. Perera, T. Aung, N. G. Strouthidis, D. Milea, and M. J. A. Girard, “In vivo 3-dimensional strain mapping confirms large optic nerve head deformations following horizontal eye movements,” Invest. Ophthalmol. Vis. Sci. 57(13), 5825–5833 (2016).
[Crossref] [PubMed]

M. J. Girard, M. R. Beotra, K. S. Chin, A. Sandhu, M. Clemo, E. Nikita, D. S. Kamal, M. Papadopoulos, J. M. Mari, T. Aung, and N. G. Strouthidis, “In vivo 3-dimensional strain mapping of the optic nerve head following intraocular pressure lowering by trabeculectomy,” Ophthalmology 123(6), 1190–1200 (2016).
[Crossref] [PubMed]

Bhagat, P. R.

P. R. Bhagat, K. V. Deshpande, and B. Natu, “Utility of ganglion cell complex analysis in early diagnosis and monitoring of glaucoma using a different spectral domain optical coherence tomography,” J Curr Glaucoma Pract 8(3), 101–106 (2014).
[Crossref] [PubMed]

Bi, H.

X. Sui, Y. Zheng, B. Wei, H. Bi, J. Wu, X. Pan, Y. Yin, and S. Zhang, “Choroid segmentation from Optical Coherence Tomography with graph-edge weights learned from deep convolutional neural networks,” Neurocomputing 237, 332–341 (2017).
[Crossref]

Bowd, C.

C. Bowd, R. N. Weinreb, J. M. Williams, and L. M. Zangwill, “The retinal nerve fiber layer thickness in ocular hypertensive, normal, and glaucomatous eyes with optical coherence tomography,” Arch. Ophthalmol. 118(1), 22–26 (2000).
[Crossref] [PubMed]

A. Belghith, C. Bowd, F. A. Medeiros, R. N. Weinreb, and L. M. Zangwill, “Automated segmentation of anterior lamina cribrosa surface: How the lamina cribrosa responds to intraocular pressure change in glaucoma eyes?” in Proceedings of IEEE 12th International Symposium on Biomedical Imaging (ISBI) (2015), 222–225.
[Crossref]

Brumm, J.

S. C. Park, J. Brumm, R. L. Furlanetto, C. Netto, Y. Liu, C. Tello, J. M. Liebmann, and R. Ritch, “Lamina Cribrosa Depth in Different Stages of Glaucoma,” Invest. Ophthalmol. Vis. Sci. 56(3), 2059–2064 (2015).
[Crossref] [PubMed]

Burgoyne, C. F.

H. Yang, G. Williams, J. C. Downs, I. A. Sigal, M. D. Roberts, H. Thompson, and C. F. Burgoyne, “Posterior (outward) migration of the lamina cribrosa and early cupping in monkey experimental glaucoma,” Invest. Ophthalmol. Vis. Sci. 52(10), 7109–7121 (2011).
[Crossref] [PubMed]

I. A. Sigal, H. Yang, M. D. Roberts, J. L. Grimm, C. F. Burgoyne, S. Demirel, and J. C. Downs, “IOP-induced lamina cribrosa deformation and scleral canal expansion: independent or related?” Invest. Ophthalmol. Vis. Sci. 52(12), 9023–9032 (2011).
[Crossref] [PubMed]

N. G. Strouthidis, J. Grimm, G. A. Williams, G. A. Cull, D. J. Wilson, and C. F. Burgoyne, “A comparison of optic nerve head morphology viewed by spectral domain optical coherence tomography and by serial histology,” Invest. Ophthalmol. Vis. Sci. 51(3), 1464–1474 (2010).
[Crossref] [PubMed]

J. C. Downs, M. E. Ensor, A. J. Bellezza, H. W. Thompson, R. T. Hart, and C. F. Burgoyne, “Posterior scleral thickness in perfusion-fixed normal and early-glaucoma monkey eyes,” Invest. Ophthalmol. Vis. Sci. 42(13), 3202–3208 (2001).
[PubMed]

Calabresi, P. A.

A. Lang, A. Carass, B. M. Jedynak, S. D. Solomon, P. A. Calabresi, and J. L. Prince, “Intensity inhomogeneity correction of macular OCT using N3 and retinal flatspace,” Proc. IEEE Int. Symp. Biomed. Imaging 2016, 197–200 (2016).
[PubMed]

A. Lang, A. Carass, M. Hauser, E. S. Sotirchos, P. A. Calabresi, H. S. Ying, and J. L. Prince, “Retinal layer segmentation of macular OCT images using boundary classification,” Biomed. Opt. Express 4(7), 1133–1152 (2013).
[Crossref] [PubMed]

Campbell, I. C.

I. C. Campbell, B. Coudrillier, J. Mensah, R. L. Abel, and C. R. Ethier, “Automated segmentation of the lamina cribrosa using Frangi’s filter: a novel approach for rapid identification of tissue volume fraction and beam orientation in a trabeculated structure in the eye,” J. R. Soc. Interface 12(104), 20141009 (2015).
[Crossref] [PubMed]

Carass, A.

A. Lang, A. Carass, B. M. Jedynak, S. D. Solomon, P. A. Calabresi, and J. L. Prince, “Intensity inhomogeneity correction of macular OCT using N3 and retinal flatspace,” Proc. IEEE Int. Symp. Biomed. Imaging 2016, 197–200 (2016).
[PubMed]

A. Lang, A. Carass, M. Hauser, E. S. Sotirchos, P. A. Calabresi, H. S. Ying, and J. L. Prince, “Retinal layer segmentation of macular OCT images using boundary classification,” Biomed. Opt. Express 4(7), 1133–1152 (2013).
[Crossref] [PubMed]

Casanova, M. F.

E. Hosseini-Asl, M. Ghazal, A. Mahmoud, A. Aslantas, A. M. Shalaby, M. F. Casanova, G. N. Barnes, G. Gimel’farb, R. Keynton, and A. El-Baz, “Alzheimer’s disease diagnostics by a 3D deeply supervised adaptable convolutional network,” Front. Biosci. (Landmark Ed.) 23(2), 584–596 (2018).
[Crossref] [PubMed]

Chauhan, B. C.

F. A. Almobarak, N. O’Leary, A. S. C. Reis, G. P. Sharpe, D. M. Hutchison, M. T. Nicolela, and B. C. Chauhan, “Automated segmentation of optic nerve head structures with optical coherence tomography,” Invest. Ophthalmol. Vis. Sci. 55(2), 1161–1168 (2014).
[Crossref] [PubMed]

Chen, Q.

S. Niu, Q. Chen, L. de Sisternes, D. L. Rubin, W. Zhang, and Q. Liu, “Automated retinal layers segmentation in SD-OCT images using dual-gradient and spatial correlation smoothness constraint,” Comput. Biol. Med. 54, 116–128 (2014).
[Crossref] [PubMed]

Chen, T. C.

Y. Liu, H. Simavli, C. J. Que, J. L. Rizzo, E. Tsikata, R. Maurer, and T. C. Chen, “Patient characteristics associated with artifacts in Spectralis optical coherence tomography imaging of the retinal nerve fiber layer in glaucoma,” Am. J. Ophthalmol. 159(3), 565–576 (2015).
[Crossref] [PubMed]

Cheng, C. Y.

T. A. Tun, E. Atalay, M. Baskaran, M. E. Nongpiur, H. M. Htoon, D. Goh, C. Y. Cheng, S. A. Perera, T. Aung, N. G. Strouthidis, and M. J. A. Girard, “Association of functional loss with the biomechanical response of the optic nerve head to acute transient intraocular pressure elevations,” JAMA Ophthalmol. 136(2), 184–192 (2018).
[Crossref] [PubMed]

T. A. Tun, S. G. Thakku, O. Png, M. Baskaran, H. M. Htoon, S. Sharma, M. E. Nongpiur, C. Y. Cheng, T. Aung, N. G. Strouthidis, and M. J. Girard, “Shape changes of the anterior lamina cribrosa in normal, ocular hypertensive, and glaucomatous eyes following acute intraocular pressure elevation,” Invest. Ophthalmol. Vis. Sci. 57(11), 4869–4877 (2016).
[Crossref] [PubMed]

S. G. Thakku, Y. C. Tham, M. Baskaran, J. M. Mari, N. G. Strouthidis, T. Aung, C. Y. Cheng, and M. J. Girard, “A global shape index to characterize anterior lamina cribrosa morphology and its determinants in healthy indian eyes,” Invest. Ophthalmol. Vis. Sci. 56(6), 3604–3614 (2015).
[Crossref] [PubMed]

Cheng, C.-Y.

J.-M. Mari, T. Aung, C.-Y. Cheng, N. G. Strouthidis, and M. J. A. Girard, “A digital staining algorithm for optical coherence tomography images of the optic nerve head,” Transl. Vis. Sci. Technol. 6(1), 8 (2017).
[Crossref] [PubMed]

M. H. Tan, S. H. Ong, S. G. Thakku, C.-Y. Cheng, T. Aung, and M. Girard, “Automatic feature extraction of optical coherence tomography for lamina cribrosa detection,” Journal of Image and Graphics 3, 2 (2015)

Chhablani, J.

R. A. Alshareef, S. Dumpala, S. Rapole, M. Januwada, A. Goud, H. K. Peguda, and J. Chhablani, “Prevalence and distribution of segmentation errors in macular ganglion cell analysis of healthy eyes using cirrus HD-OCT,” PLoS One 11(5), e0155319 (2016).
[Crossref] [PubMed]

Chin, K. S.

S. K. Devalla, K. S. Chin, J. M. Mari, T. A. Tun, N. G. Strouthidis, T. Aung, A. H. Thiéry, and M. J. A. Girard, “A deep learning approach to digitally stain optical coherence tomography images of the optic nerve head,” Invest. Ophthalmol. Vis. Sci. 59(1), 63–74 (2018).
[Crossref] [PubMed]

M. J. Girard, M. R. Beotra, K. S. Chin, A. Sandhu, M. Clemo, E. Nikita, D. S. Kamal, M. Papadopoulos, J. M. Mari, T. Aung, and N. G. Strouthidis, “In vivo 3-dimensional strain mapping of the optic nerve head following intraocular pressure lowering by trabeculectomy,” Ophthalmology 123(6), 1190–1200 (2016).
[Crossref] [PubMed]

Choi, D. Y.

J. C. Han, D. Y. Choi, Y. K. Kwun, W. Suh, and C. Kee, “Evaluation of lamina cribrosa thickness and depth in ocular hypertension,” Jpn. J. Ophthalmol. 60(1), 14–19 (2016).
[Crossref] [PubMed]

Clemo, M.

M. J. Girard, M. R. Beotra, K. S. Chin, A. Sandhu, M. Clemo, E. Nikita, D. S. Kamal, M. Papadopoulos, J. M. Mari, T. Aung, and N. G. Strouthidis, “In vivo 3-dimensional strain mapping of the optic nerve head following intraocular pressure lowering by trabeculectomy,” Ophthalmology 123(6), 1190–1200 (2016).
[Crossref] [PubMed]

Collins, M. J.

Coudrillier, B.

I. C. Campbell, B. Coudrillier, J. Mensah, R. L. Abel, and C. R. Ethier, “Automated segmentation of the lamina cribrosa using Frangi’s filter: a novel approach for rapid identification of tissue volume fraction and beam orientation in a trabeculated structure in the eye,” J. R. Soc. Interface 12(104), 20141009 (2015).
[Crossref] [PubMed]

Cull, G. A.

N. G. Strouthidis, J. Grimm, G. A. Williams, G. A. Cull, D. J. Wilson, and C. F. Burgoyne, “A comparison of optic nerve head morphology viewed by spectral domain optical coherence tomography and by serial histology,” Invest. Ophthalmol. Vis. Sci. 51(3), 1464–1474 (2010).
[Crossref] [PubMed]

Cunefare, D.

de Sisternes, L.

S. Niu, Q. Chen, L. de Sisternes, D. L. Rubin, W. Zhang, and Q. Liu, “Automated retinal layers segmentation in SD-OCT images using dual-gradient and spatial correlation smoothness constraint,” Comput. Biol. Med. 54, 116–128 (2014).
[Crossref] [PubMed]

Demirel, S.

S. L. Mansberger, S. A. Menda, B. A. Fortune, S. K. Gardiner, and S. Demirel, “Automated segmentation errors when using optical coherence tomography to measure retinal nerve fiber layer thickness in glaucoma,” Am. J. Ophthalmol. 174, 1–8 (2017).
[Crossref] [PubMed]

S. K. Gardiner, S. Demirel, J. Reynaud, and B. Fortune, “Changes in retinal nerve fiber layer reflectance intensity as a predictor of functional progression in glaucoma,” Invest. Ophthalmol. Vis. Sci. 57(3), 1221–1227 (2016).
[Crossref] [PubMed]

I. A. Sigal, H. Yang, M. D. Roberts, J. L. Grimm, C. F. Burgoyne, S. Demirel, and J. C. Downs, “IOP-induced lamina cribrosa deformation and scleral canal expansion: independent or related?” Invest. Ophthalmol. Vis. Sci. 52(12), 9023–9032 (2011).
[Crossref] [PubMed]

Deshpande, K. V.

P. R. Bhagat, K. V. Deshpande, and B. Natu, “Utility of ganglion cell complex analysis in early diagnosis and monitoring of glaucoma using a different spectral domain optical coherence tomography,” J Curr Glaucoma Pract 8(3), 101–106 (2014).
[Crossref] [PubMed]

Desjardins, A.

M. J. Girard, N. G. Strouthidis, A. Desjardins, J. M. Mari, and C. R. Ethier, “In vivo optic nerve head biomechanics: performance testing of a three-dimensional tracking algorithm,” J. R. Soc. Interface 10(87), 20130459 (2013).
[Crossref] [PubMed]

Devalla, S. K.

S. K. Devalla, K. S. Chin, J. M. Mari, T. A. Tun, N. G. Strouthidis, T. Aung, A. H. Thiéry, and M. J. A. Girard, “A deep learning approach to digitally stain optical coherence tomography images of the optic nerve head,” Invest. Ophthalmol. Vis. Sci. 59(1), 63–74 (2018).
[Crossref] [PubMed]

DeZonia, B. E.

C. T. S. Rueden, J. Schindelin, M. C. Hiner, B. E. DeZonia, A. E. Walter, E. T. Arena, and K. W. Eliceiri, “ImageJ2: ImageJ for the next generation of scientific image data,” BMC Bioinformatics 18(1), 529 (2017).
[Crossref] [PubMed]

Diniz-Filho, A.

R. Y. Abe, C. P. B. Gracitelli, A. Diniz-Filho, A. J. Tatham, and F. A. Medeiros, “Lamina Cribrosa in Glaucoma: Diagnosis and Monitoring,” Curr. Ophthalmol. Rep. 3(2), 74–84 (2015).
[Crossref] [PubMed]

Downs, J. C.

I. A. Sigal, H. Yang, M. D. Roberts, J. L. Grimm, C. F. Burgoyne, S. Demirel, and J. C. Downs, “IOP-induced lamina cribrosa deformation and scleral canal expansion: independent or related?” Invest. Ophthalmol. Vis. Sci. 52(12), 9023–9032 (2011).
[Crossref] [PubMed]

H. Yang, G. Williams, J. C. Downs, I. A. Sigal, M. D. Roberts, H. Thompson, and C. F. Burgoyne, “Posterior (outward) migration of the lamina cribrosa and early cupping in monkey experimental glaucoma,” Invest. Ophthalmol. Vis. Sci. 52(10), 7109–7121 (2011).
[Crossref] [PubMed]

J. C. Downs, M. E. Ensor, A. J. Bellezza, H. W. Thompson, R. T. Hart, and C. F. Burgoyne, “Posterior scleral thickness in perfusion-fixed normal and early-glaucoma monkey eyes,” Invest. Ophthalmol. Vis. Sci. 42(13), 3202–3208 (2001).
[PubMed]

Dumpala, S.

R. A. Alshareef, S. Dumpala, S. Rapole, M. Januwada, A. Goud, H. K. Peguda, and J. Chhablani, “Prevalence and distribution of segmentation errors in macular ganglion cell analysis of healthy eyes using cirrus HD-OCT,” PLoS One 11(5), e0155319 (2016).
[Crossref] [PubMed]

El-Baz, A.

E. Hosseini-Asl, M. Ghazal, A. Mahmoud, A. Aslantas, A. M. Shalaby, M. F. Casanova, G. N. Barnes, G. Gimel’farb, R. Keynton, and A. El-Baz, “Alzheimer’s disease diagnostics by a 3D deeply supervised adaptable convolutional network,” Front. Biosci. (Landmark Ed.) 23(2), 584–596 (2018).
[Crossref] [PubMed]

Eliceiri, K. W.

C. T. S. Rueden, J. Schindelin, M. C. Hiner, B. E. DeZonia, A. E. Walter, E. T. Arena, and K. W. Eliceiri, “ImageJ2: ImageJ for the next generation of scientific image data,” BMC Bioinformatics 18(1), 529 (2017).
[Crossref] [PubMed]

Ensor, M. E.

J. C. Downs, M. E. Ensor, A. J. Bellezza, H. W. Thompson, R. T. Hart, and C. F. Burgoyne, “Posterior scleral thickness in perfusion-fixed normal and early-glaucoma monkey eyes,” Invest. Ophthalmol. Vis. Sci. 42(13), 3202–3208 (2001).
[PubMed]

Essaid, L.

S. Asrani, L. Essaid, B. D. Alder, and C. Santiago-Turla, “Artifacts in spectral-domain optical coherence tomography measurements in glaucoma,” JAMA Ophthalmol. 132(4), 396–402 (2014).
[Crossref] [PubMed]

Ethier, C. R.

I. C. Campbell, B. Coudrillier, J. Mensah, R. L. Abel, and C. R. Ethier, “Automated segmentation of the lamina cribrosa using Frangi’s filter: a novel approach for rapid identification of tissue volume fraction and beam orientation in a trabeculated structure in the eye,” J. R. Soc. Interface 12(104), 20141009 (2015).
[Crossref] [PubMed]

M. J. Girard, N. G. Strouthidis, A. Desjardins, J. M. Mari, and C. R. Ethier, “In vivo optic nerve head biomechanics: performance testing of a three-dimensional tracking algorithm,” J. R. Soc. Interface 10(87), 20130459 (2013).
[Crossref] [PubMed]

Fan, N.

N. Fan, N. Huang, D. S. C. Lam, and C. K.-S. Leung, “Measurement of photoreceptor layer in glaucoma: a spectral-domain optical coherence tomography study,” J. Ophthalmol. 2011, 264803 (2011).
[Crossref] [PubMed]

Fang, L.

Farsiu, S.

Fauser, S.

Fortune, B.

S. K. Gardiner, S. Demirel, J. Reynaud, and B. Fortune, “Changes in retinal nerve fiber layer reflectance intensity as a predictor of functional progression in glaucoma,” Invest. Ophthalmol. Vis. Sci. 57(3), 1221–1227 (2016).
[Crossref] [PubMed]

Fortune, B. A.

S. L. Mansberger, S. A. Menda, B. A. Fortune, S. K. Gardiner, and S. Demirel, “Automated segmentation errors when using optical coherence tomography to measure retinal nerve fiber layer thickness in glaucoma,” Am. J. Ophthalmol. 174, 1–8 (2017).
[Crossref] [PubMed]

Furlanetto, R. L.

S. C. Park, J. Brumm, R. L. Furlanetto, C. Netto, Y. Liu, C. Tello, J. M. Liebmann, and R. Ritch, “Lamina Cribrosa Depth in Different Stages of Glaucoma,” Invest. Ophthalmol. Vis. Sci. 56(3), 2059–2064 (2015).
[Crossref] [PubMed]

Gardiner, S. K.

S. L. Mansberger, S. A. Menda, B. A. Fortune, S. K. Gardiner, and S. Demirel, “Automated segmentation errors when using optical coherence tomography to measure retinal nerve fiber layer thickness in glaucoma,” Am. J. Ophthalmol. 174, 1–8 (2017).
[Crossref] [PubMed]

S. K. Gardiner, S. Demirel, J. Reynaud, and B. Fortune, “Changes in retinal nerve fiber layer reflectance intensity as a predictor of functional progression in glaucoma,” Invest. Ophthalmol. Vis. Sci. 57(3), 1221–1227 (2016).
[Crossref] [PubMed]

Garvin, M. K.

M. S. Miri, M. D. Abràmoff, Y. H. Kwon, M. Sonka, and M. K. Garvin, “A machine-learning graph-based approach for 3D segmentation of Bruch’s membrane opening from glaucomatous SD-OCT volumes,” Med. Image Anal. 39, 206–217 (2017).
[Crossref] [PubMed]

Ghazal, M.

E. Hosseini-Asl, M. Ghazal, A. Mahmoud, A. Aslantas, A. M. Shalaby, M. F. Casanova, G. N. Barnes, G. Gimel’farb, R. Keynton, and A. El-Baz, “Alzheimer’s disease diagnostics by a 3D deeply supervised adaptable convolutional network,” Front. Biosci. (Landmark Ed.) 23(2), 584–596 (2018).
[Crossref] [PubMed]

Gimel’farb, G.

E. Hosseini-Asl, M. Ghazal, A. Mahmoud, A. Aslantas, A. M. Shalaby, M. F. Casanova, G. N. Barnes, G. Gimel’farb, R. Keynton, and A. El-Baz, “Alzheimer’s disease diagnostics by a 3D deeply supervised adaptable convolutional network,” Front. Biosci. (Landmark Ed.) 23(2), 584–596 (2018).
[Crossref] [PubMed]

Girard, M.

M. H. Tan, S. H. Ong, S. G. Thakku, C.-Y. Cheng, T. Aung, and M. Girard, “Automatic feature extraction of optical coherence tomography for lamina cribrosa detection,” Journal of Image and Graphics 3, 2 (2015)

Girard, M. J.

M. J. Girard, M. R. Beotra, K. S. Chin, A. Sandhu, M. Clemo, E. Nikita, D. S. Kamal, M. Papadopoulos, J. M. Mari, T. Aung, and N. G. Strouthidis, “In vivo 3-dimensional strain mapping of the optic nerve head following intraocular pressure lowering by trabeculectomy,” Ophthalmology 123(6), 1190–1200 (2016).
[Crossref] [PubMed]

T. A. Tun, S. G. Thakku, O. Png, M. Baskaran, H. M. Htoon, S. Sharma, M. E. Nongpiur, C. Y. Cheng, T. Aung, N. G. Strouthidis, and M. J. Girard, “Shape changes of the anterior lamina cribrosa in normal, ocular hypertensive, and glaucomatous eyes following acute intraocular pressure elevation,” Invest. Ophthalmol. Vis. Sci. 57(11), 4869–4877 (2016).
[Crossref] [PubMed]

M. J. Girard, T. A. Tun, R. Husain, S. Acharyya, B. A. Haaland, X. Wei, J. M. Mari, S. A. Perera, M. Baskaran, T. Aung, and N. G. Strouthidis, “Lamina cribrosa visibility using optical coherence tomography: comparison of devices and effects of image enhancement techniques,” Invest. Ophthalmol. Vis. Sci. 56(2), 865–874 (2015).
[Crossref] [PubMed]

S. G. Thakku, Y. C. Tham, M. Baskaran, J. M. Mari, N. G. Strouthidis, T. Aung, C. Y. Cheng, and M. J. Girard, “A global shape index to characterize anterior lamina cribrosa morphology and its determinants in healthy indian eyes,” Invest. Ophthalmol. Vis. Sci. 56(6), 3604–3614 (2015).
[Crossref] [PubMed]

M. J. Girard, N. G. Strouthidis, A. Desjardins, J. M. Mari, and C. R. Ethier, “In vivo optic nerve head biomechanics: performance testing of a three-dimensional tracking algorithm,” J. R. Soc. Interface 10(87), 20130459 (2013).
[Crossref] [PubMed]

Girard, M. J. A.

S. K. Devalla, K. S. Chin, J. M. Mari, T. A. Tun, N. G. Strouthidis, T. Aung, A. H. Thiéry, and M. J. A. Girard, “A deep learning approach to digitally stain optical coherence tomography images of the optic nerve head,” Invest. Ophthalmol. Vis. Sci. 59(1), 63–74 (2018).
[Crossref] [PubMed]

T. A. Tun, E. Atalay, M. Baskaran, M. E. Nongpiur, H. M. Htoon, D. Goh, C. Y. Cheng, S. A. Perera, T. Aung, N. G. Strouthidis, and M. J. A. Girard, “Association of functional loss with the biomechanical response of the optic nerve head to acute transient intraocular pressure elevations,” JAMA Ophthalmol. 136(2), 184–192 (2018).
[Crossref] [PubMed]

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M. J. Girard, M. R. Beotra, K. S. Chin, A. Sandhu, M. Clemo, E. Nikita, D. S. Kamal, M. Papadopoulos, J. M. Mari, T. Aung, and N. G. Strouthidis, “In vivo 3-dimensional strain mapping of the optic nerve head following intraocular pressure lowering by trabeculectomy,” Ophthalmology 123(6), 1190–1200 (2016).
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C. Ye, M. Yu, and C. K. Leung, “Impact of segmentation errors and retinal blood vessels on retinal nerve fibre layer measurements using spectral-domain optical coherence tomography,” Acta Ophthalmol. 94(3), e211–e219 (2016).
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Am. J. Ophthalmol. (2)

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L. Fang, D. Cunefare, C. Wang, R. H. Guymer, S. Li, and S. Farsiu, “Automatic segmentation of nine retinal layer boundaries in OCT images of non-exudative AMD patients using deep learning and graph search,” Biomed. Opt. Express 8(5), 2732–2744 (2017).
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F. G. Venhuizen, B. van Ginneken, B. Liefers, M. J. J. P. van Grinsven, S. Fauser, C. Hoyng, T. Theelen, and C. I. Sánchez, “Robust total retina thickness segmentation in optical coherence tomography images using convolutional neural networks,” Biomed. Opt. Express 8(7), 3292–3316 (2017).
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BMC Bioinformatics (1)

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Comput. Biol. Med. (1)

S. Niu, Q. Chen, L. de Sisternes, D. L. Rubin, W. Zhang, and Q. Liu, “Automated retinal layers segmentation in SD-OCT images using dual-gradient and spatial correlation smoothness constraint,” Comput. Biol. Med. 54, 116–128 (2014).
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R. Y. Abe, C. P. B. Gracitelli, A. Diniz-Filho, A. J. Tatham, and F. A. Medeiros, “Lamina Cribrosa in Glaucoma: Diagnosis and Monitoring,” Curr. Ophthalmol. Rep. 3(2), 74–84 (2015).
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Front. Biosci. (Landmark Ed.) (1)

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J Curr Glaucoma Pract (1)

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Z. Lin, S. Huang, B. Xie, and Y. Zhong, “Peripapillary choroidal thickness and open-angle glaucoma: a meta-analysis,” J. Ophthalmol. 2016, 1 (2016).
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I. C. Campbell, B. Coudrillier, J. Mensah, R. L. Abel, and C. R. Ethier, “Automated segmentation of the lamina cribrosa using Frangi’s filter: a novel approach for rapid identification of tissue volume fraction and beam orientation in a trabeculated structure in the eye,” J. R. Soc. Interface 12(104), 20141009 (2015).
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M. H. Tan, S. H. Ong, S. G. Thakku, C.-Y. Cheng, T. Aung, and M. Girard, “Automatic feature extraction of optical coherence tomography for lamina cribrosa detection,” Journal of Image and Graphics 3, 2 (2015)

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T. Ojima, T. Tanabe, M. Hangai, S. Yu, S. Morishita, and N. Yoshimura, “Measurement of retinal nerve fiber layer thickness and macular volume for glaucoma detection using optical coherence tomography,” Jpn. J. Ophthalmol. 51(3), 197–203 (2007).
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Neurocomputing (1)

X. Sui, Y. Zheng, B. Wei, H. Bi, J. Wu, X. Pan, Y. Yin, and S. Zhang, “Choroid segmentation from Optical Coherence Tomography with graph-edge weights learned from deep convolutional neural networks,” Neurocomputing 237, 332–341 (2017).
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Ophthalmology (4)

Z. Wu, G. Xu, R. N. Weinreb, M. Yu, and C. K. Leung, “Optic nerve head deformation in glaucoma: a prospective analysis of optic nerve head surface and lamina cribrosa surface displacement,” Ophthalmology 122(7), 1317–1329 (2015).
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M. J. Girard, M. R. Beotra, K. S. Chin, A. Sandhu, M. Clemo, E. Nikita, D. S. Kamal, M. Papadopoulos, J. M. Mari, T. Aung, and N. G. Strouthidis, “In vivo 3-dimensional strain mapping of the optic nerve head following intraocular pressure lowering by trabeculectomy,” Ophthalmology 123(6), 1190–1200 (2016).
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A. Miki, F. A. Medeiros, R. N. Weinreb, S. Jain, F. He, L. Sharpsten, N. Khachatryan, N. Hammel, J. M. Liebmann, C. A. Girkin, P. A. Sample, and L. M. Zangwill, “Rates of retinal nerve fiber layer thinning in glaucoma suspect eyes,” Ophthalmology 121(7), 1350–1358 (2014).
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Figures (9)

Fig. 1
Fig. 1 Manual segmentation of a compensated OCT image. The RNFL and the prelaminar tissue are shown in red, the RPE in pink, all other retinal layers in cyan, the choroid in green, the peripapillary sclera in yellow, the LC in blue, noise in grey and the vitreous humor in black.
Fig. 2
Fig. 2 DRUNET comprises of two towers: (1) A downsampling tower – to capture the contextual information (i.e., spatial arrangement of the tissues), and (2) an upsampling tower – to capture the local information (i.e., tissue texture). Each tower consists of two blocks: (1) a standard block, and (2) a residual block. The entire network consists of 40,000 trainable parameters in total.
Fig. 3
Fig. 3 Extensive data augmentation was performed to overcome the sparsity of our training data. (A) represents a compensated OCT image of a glaucoma subject. (B) represents the same image having undergone data augmentation. The data augmentation includes horizontal flipping, rotation (8 degrees clockwise), additive white noise and multiplicative speckle noise [44], elastic deformation [45] and occluding patches. A portion of the image undergoing elastic deformation and occlusion from patches is bounded by blue and red box respectively. The elastic deformations (combination of shearing and stretching) made our network invariant to images with atypical morphology (i.e., ONH tissue deformation in glaucoma [47]). The occluding patches reduced visibility of certain tissues, making our network invariant to blood vessel shadows.
Fig. 4
Fig. 4 Automated extraction of the ONH structural parameters. Upon segmenting the ONH tissues, six neural and connective tissue structural parameters were automatically extracted: (1) the disc diameter, (2) peripapillary RNFL thickness (p-RNFLT), (3) peripapillary choroidal thickness (p-CT), (4) minimum rim width (MRW), (5) prelaminar thickness (PLT), and the (6) prelaminar depth (PLD).
Fig. 5
Fig. 5 Baseline (1st row), compensated (2nd row), manually segmented (3rd row), DRUNET segmented images (trained on 40 compensated images; 4th row), and DRUNET segmented images (trained on 40 baseline images; 5th row) for 4 selected subjects (1&2: POAG, 3: Healthy, 4: PACG).
Fig. 6
Fig. 6 A quantitative analysis of the proposed method is presented to assess the consistency and segmentation performance between glaucoma and healthy images. A total of 5 data sets were used for training (40 images) and its corresponding testing (60 images). (A-C) represent the Dice coefficients, sensitivities and specificities as box plots for the RNFL + prelamina for healthy (in green) and glaucoma (in yellow) images in the testing sets. (D-F) represent the same for the RPE, (G-I) represent the same for all other retinal layers and (J-L) represent the same for the choroid.
Fig. 7
Fig. 7 The effect of compensation on the segmentation accuracy is presented. A total of 5 compensated and uncompensated data sets were used for training (40 images) and its corresponding testing (60 images). Box plots (1-4) represent the mean of the 5 compensated (normal in green; glaucoma in yellow) and uncompensated (normal in cyan; glaucoma in red) data sets. (A-C) represent the Dice coefficients, sensitivities and specificities for the RNFL + prelamina. (D-F) represent the same for the RPE, (G-I) represent the same for all other retinal layers and (J-L) represent the same for the choroid.
Fig. 8
Fig. 8 In an attempt to understand the significance of each design element in the DRUNET better, four different architectures were trained with and without data augmentation. Architecture 1: Baseline U-Net (each tower consisted of only standard blocks; standard convolution layers); Architecture 2: Modified U-Net v1 (each tower consisted of one standard block and two residual blocks; standard convolution layers); Architecture 3: Modified U-Net v2 (each tower consisted of one standard block and two residual blocks with standard convolution layers; batch normalization after every convolution layer in the residual block); Architecture 4: DRUNET (each tower consisted of one standard block and two residual blocks with dilated convolution layers; batch normalization after every dilated convolution layer in the residual block). (A & B) represent the training loss and the validation accuracy respectively for all the four architectures, when trained with data augmentation. (C & D) represent the same, when trained without data augmentation.
Fig. 9
Fig. 9 Baseline (1st row), manually segmented (2nd row), and DRUNET segmented images (3rd row) for 4 selected subjects (1&2: POAG, 3: Healthy, 4: PACG).

Tables (3)

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Table 1 Performance Comparison Between DRUNET and Patch-Based Segmentation

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Table 2 Clinical Application: Automated Extraction of Structural Parameters of the ONH

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Table 3 DRUNET Robustness

Equations (7)

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J i = i=1 N | P i T i | | P i T i |
L=1 J N
I ¯ =a+( a+b+1 )× I p
P w = P o +αu
D C i = 2 *  | D S i M S i | | D S i |+| M S i |
S. p i = | D S i ¯ M S i ¯ | | M S i ¯ |
S. n i =  | D S i M S i | |  M S i |

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